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Summary Background A wait-and-see policy (WS) does not appear to modify the long-term prognosis of advanced-stage follicular lymphomas (FL), while irradiation of limited stages some times causes complications and does not avert distant relapses. Consequently, we decided to test WS in a selected su bset of the localized FL, i.e., patients in complete remission (CR) after the initial lymph node biopsy (stage I0). Patients and methods Forty-three previously untreated patients were diagnosed with stage I0 FL and 26 of them were included in the WS. Their median age was 60.3 years; 19 were male and 24 female. All histological slides were reviewed and confirmed the diagnosis of FL. Median followup was 6.3 years (y). Results Thirteen of the 26 untreated patients are still relapse-free, while six relapsed locally only (median: 4.2 years after diagnosis), and reattained CR with radiotherapy. Seven patients relapsed at distant sites (median: 1 year after diagnosis). No localized relapses were observed in the treated group, but there were 7 distant relapses. Conclusions The use of use in stage I0 FL did not appear to modify the prognosis of these patients. Furthermore, we observed two distinct patterns of relapse (local and distant) that are difficult to differentiate at onset.

The paper focuses on the definition, calibration and testing of a simulation model that is able to represent multimodal choice behaviours for electric vehicles. Taking into account the interchange between public transport and electric private mobility, the model estimates the parking demand at the Park & Ride sites equipped with charging stations. The model is based on a data-driven approach, in which mainly Floating Car Data and open data of public transport have derived the explanatory variables. Specifically, a machine learning method (Random Forest) has been used to calibrate and test the model in the real case of the metropolitan area of Rome (Italy). We first perform a stability analysis, letting the parameters of the model vary. We then carry out a sensitivity analysis on the variables that can affect the user propensity to adopt the Park & Ride. Finally, we profile and test an incentive policy to boost the choice of Park & Ride. Results suggest that the model succeeds in simulating Park & Ride by electric vehicles and, therefore, it can be extremely valuable for planning financial support to the multimodal travel choice and forecasting vehicle-to-grid scenarios.